Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

The paper proposes Metropolis adjusted Langevin and Hamiltonian Monte Carlo sampling
methods defined on the Riemann manifold to resolve the shortcomings of existing Monte …

Probabilistic modeling is iterative. A scientist posits a simple model, fits it to her data, refines
it according to her analysis, and repeats. However, fitting complex models to large data is a …

This paper exploits a data rich environment to provide direct econometric estimates of time-
varying macroeconomic uncertainty. Our estimates display significant independent …

The COVID-19 pandemic has led to enormous data movements that strongly affect
parameters and forecasts from standard Bayesian vector autoregressions (BVARs). To …

We propose a new model for measuring uncertainty and its effects on the economy, based
on a large vector autoregression with stochastic volatility driven by common factors …

Provides a comprehensive and updated study of GARCH models and their applications in
finance, covering new developments in the discipline This book provides a comprehensive …

Structured additive regression models are perhaps the most commonly used class of models
in statistical applications. It includes, among others,(generalized) linear …

Monte Carlo statistical methods, particularly those based on Markov chains, are now an
essential component of the standard set of techniques used by statisticians. This new edition …

Provides statistical tools and techniques needed to understand today's financial markets The
Second Edition of this critically acclaimed text provides a comprehensive and systematic …